Vertical Composition Variation with Multiple Samples for Reservoir Model Initialization

Abstract

Abstract A challenging step in reservoir modeling is capturing fluid composition variation. This is a complex task as fluid samples taken from wells in different areas of the reservoir usually have large areal and vertical compositional variation. Modeling representative composition variation with depth in the presence of multiple samples is critical for reservoir simulation and hydrocarbon initially in place assessment, and, on the other hand, a technically challenging task. In this paper, we present an automated workflow integrated in a commercial exploration and production (E&P) software that addresses compositional variation for reservoir simulation model initialization for multiple fluid samples. Composition variation with depth requires a depth window, number of depth points, composition, temperature, pressure and reference depth for all fluid samples. Using a specific equation of state (EoS), the workflow is executed for every fluid sample by performing compositional variation with depth based on Gibbs conditions for thermodynamic equilibrium. The output of this step is a composition variation with depth distribution for every fluid sample. Finally, the best-matching model is chosen by comparing each model results with the data for all existing fluid samples. The proposed workflow was tested using a specific EoS in a reservoir with several fluid samples. One composition variation with depth model was generated for every fluid sample. In the next step, all models were evaluated by calculating the average errors between the model and each fluid sample. Finally, the best-matching models were selected, and the results were evaluated. It was observed that the best-matching models were able to accurately predict the pressure and saturation pressure for large number of fluid samples. The proposed workflow was also integrated into an industry-leading E&P modeling software platform to serve as an automated functionality that outputs the required files to perform initialization with equilibration of the dynamic reservoir model. Capturing fluid composition variation in the reservoir is an important step in reservoir modeling. The proposed work presents an automated workflow that generates the best-matching composition variation with depth model for multiple samples. Using traditional approaches, this is a challenging and time-consuming step as fluid samples taken from wells in different areas of a reservoir can have significant areal compositional variation.